Joint Holographic Detection and Reconstruction

Florence Yellin, Benjamín Béjar, Benjamin D. Haeffele, Evelien Mathieu, Christian Pick, Stuart C. Ray, René Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHeung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan
PublisherSpringer
Pages664-672
Number of pages9
ISBN (Print)9783030326913
DOIs
StatePublished - Jan 1 2019
Event10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/13/19

Keywords

  • Convolutional sparse coding
  • Holography
  • Phase recovery

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yellin, F., Béjar, B., Haeffele, B. D., Mathieu, E., Pick, C., Ray, S. C., & Vidal, R. (2019). Joint Holographic Detection and Reconstruction. In H-I. Suk, M. Liu, C. Lian, & P. Yan (Eds.), Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 664-672). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11861 LNCS). Springer. https://doi.org/10.1007/978-3-030-32692-0_76